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All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy.
Über empirische Funktionen und die Interpolation zwischen äquidistanten Ordinaten
Runge, C · 1901
Earlier work this paper cites.
On a new method of graduation
Whittaker, E. T · 1922
Earlier work this paper cites.
On the determination of molecular fields.—I. from the variation of the viscosity of a gas with temperature
Jones, J. E · 1924
Earlier work this paper cites.
Diatomic molecules according to the wave mechanics. II. vibrational levels
Morse, P. M · 1929
Earlier work this paper cites.
The Cauchy relations in a molecular theory of elasticity
Stakgold, I · 1950
Earlier work this paper cites.
Spline functions and the problem of graduation
Schoenberg, I. J · 1964
Earlier work this paper cites.
Smoothing by spline functions
Reinsch, C. H · 1967
Earlier work this paper cites.
A Practical Guide to Splines (Springer, New York, 1978)
de Boor, C · 1978
Earlier work this paper cites.
Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals
Daw, M. S. & Baskes, M. I · 1984
Earlier work this paper cites.
Fast parallel algorithms for short-range molecular dynamics
Plimpton, S · 1995
Earlier work this paper cites.
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set
Kresse, G. & Furthmüller, J · 1996
Earlier work this paper cites.
Flexible smoothing with B-splines and penalties
Eilers, P. H. & Marx, B. D · 1996
Earlier work this paper cites.
Generalized gradient approximation made simple
Perdew, J. P., Burke, K. & Ernzerhof, M · 1996
Earlier work this paper cites.
Tabulated potentials in molecular dynamics simulations
Wolff, D. & Rudd, W · 1999
Earlier work this paper cites.
General relations between many-body potentials and cluster expansions in multicomponent systems
Drautz, R., Fähnle, M. & Sanchez, J. M · 2004
Earlier work this paper cites.
The Art of Molecular Dynamics Simulation (Cambridge University Press, 2004)
Rapaport, D · 2004
Earlier work this paper cites.
Generalized neural-network representation of high-dimensional potential-energy surfaces
Behler, J. & Parrinello, M · 2007
Earlier work this paper cites.
Classical potential describes martensitic phase transformations between the α \alpha , β \beta , and ω \omega titanium phases
Hennig, R., Lenosky, T., Trinkle, D., Rudin, S. & Wilkins, J · 2008
Earlier work this paper cites.
Pair vs many-body potentials: Influence on elastic and plastic behavior in nanoindentation of fcc metals
Ziegenhain, G., Hartmaier, A. & Urbassek, H. M · 2009
Earlier work this paper cites.
Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons
Bartók, A. P., Payne, M. C., Kondor, R. & Csányi, G · 2010
Earlier work this paper cites.
Melting temperature of tungsten from twoab initioapproaches
Wang, L. G., van de Walle, A. & Alfè, D · 2011
Earlier work this paper cites.
Fitting empirical potentials: Challenges and methodologies
Martinez, J. A., Yilmaz, D. E., Liang, T., Sinnott, S. B. & Phillpot, S. R · 2013
Earlier work this paper cites.
Interatomic potentials for modelling radiation defects and dislocations in tungsten
Marinica, M.-C. et al · 2013
Cited alongside, same era.
On representing chemical environments
Bartók, A. P., Kondor, R. & Csányi, G · 2013
Cited alongside, same era.
Considerations for choosing and using force fields and interatomic potentials in materials science and engineering
Becker, C. A., Tavazza, F., Trautt, Z. T. & Buarque De Macedo, R. A · 2013
Cited alongside, same era.
Accuracy and transferability of Gaussian approximation potential models for tungsten
Szlachta, W. J., Bartók, A. P. & Csányi, G · 2014
Cited alongside, same era.
Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
Thompson, A., Swiler, L., Trott, C., Foiles, S. & Tucker, G · 2015
Cited alongside, same era.
Interpolation effects in tabulated interatomic potentials
Fast general two- and three-body interatomic potential
Pozdnyakov, S., Oganov, A. R., Mazitov, A., Kruglov, I. & Mazhnik, E · 2020
Later among the works it cites.
SciPy 1.0: Fundamental algorithms for scientific computing in Python
Virtanen, P. et al · 2020
Later among the works it cites.
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Miksch, A. M., Morawietz, T., Kästner, J., Urban, A. & Artrith, N · 2021
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Friederich, P., Häse, F., Proppe, J. & Aspuru-Guzik, A · 2021
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Physics-inspired structural representations for molecules and materials
Musil, F. et al · 2021
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Gaussian process regression for materials and molecules
Deringer, V. L. et al · 2021
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Gaussian approximation potentials: A brief tutorial introduction
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